Databricks-Certified-Professional-Data-Scientist Exam Dumps - Databricks Certified Professional Data Scientist Exam

Yes a data scientists should have combination of skills like to solve the complex problem he should be creative as well as able to find new solutions and use of existing data. And solve the problem skills required are programming as currently we see SAS, R: Python, Spark, Java and SPSS even day by day new technologies are coming.

To apply various existing and new algorithm using Machine Learning, or Al it require good mathematics and statistics skills (Where the programmer feels, weaknesses). Another skill required is using visualization techniques like Qlik, Tableau etc

Laplace smoothing is a technique for parameter estimation which accounts for unobserved events. It is more robust and will not fail completely when data that has never been observed in training shows up.

Linear regression models a linear relationship of a scalar dependent variable y to one or more explanatory independent variables x to build a model of coefficients.

Unsupervised learning seems much harder: the goal is to have the computer learn how to do something that we don't tell it how to do! The approach is to teach the agent not by giving explicit categorizations, but by using some sort of reward system to indicate success. Note that this type of training will generally fit into the decision problem framework because the goal is not to produce a classification but to make decisions that maximize rewards. This approach nicely generalizes to the real world, where agents might be rewarded for doing certain actions and punished fordoing others.

To see how this is done, let W represent the event that the conversation was held with a woman, and L denote the event that the conversation was held with a long­haired person. It can be assumed that women constitute half the population for this example. So, not knowing anything else, the probability that W occurs is P(W) = 0.5. Suppose it is also known that 75% of women have long hair which we denote as P(L |W) = 0.75 (read: the probability of event L given event W is 0.75, meaning that the probability of a person having long hair (event "L"): given that we already know that the person is a woman ("event W") is 75%). Likewise, suppose it is known that 15% of men have long hair, or P(L |M) = 0.15; where M is the complementary event of W: i.e.; the event that the conversation was held with a man (assuming that every human is either a man or a woman).

Our goal is to calculate the probability that the conversation was held with a woman, given the fact that the person had long hair, or, in our notation, P(W |L). Using the formula for Bayes' theorem, we have:

Text Description automatically generated with low confidence

where we have used the law of total probability to expand

P(L),

The numeric answer can be obtained by substituting the above values into this formula (the algebraic multiplication is annotated using " *", the centered dot). This yields

A picture containing table Description automatically generated

i.e., the probability that the conversation was held with a woman, given that the person had long hair is about 83%. More examples are provided below.